1. Field of the Invention
This invention pertains to a system for diagnosing a risk of cardiac dysfunction based on electrocardiogram data. Specifically, this invention pertains to the use of dynamical systems modeling techniques to identify metrics used to diagnose heart health.
2. Background
An electrocardiogram (ECG) is a recording of the electrical activity of the heart over time. Cardiac cells are electrically polarized, that is, the insides of the cells are negatively charged with respect to their outside of the cells by means of pumps in the cell membrane that distribute ions (primarily potassium, sodium, chloride, and calcium) in order to keep the insides of these cells negatively charged (i.e. electronegative). Cardiac cells loose their internal electronegativity in depolarization. Depolarization is an electrical event which corresponds to heart contraction or “beating”. In depolarization, loss of electronegativity is propagated from cell to cell, producing a wave of electrical activity that can be transmitted across the heart.
Electrical impulses in the heart originate in the sinoatrial node (SA Node) and travel through the heart muscle where they impart electrical initiation of “systole” or contraction of the heart. The electrical waves can be measured by electrodes (electrical contacts) placed on the skin of a subject. These electrodes measure the electrical activity of different parts of the heart muscle. An ECG displays the voltage between pairs of these electrodes, and the muscle activity that they measure, from different directions. This display indicates the rhythm of heart contraction.
FIG. 1 depicts the peaks in an electrocardiogram signal. Electrocardiogram signals are comprised of three major structures which are used to characterize the health of a subject's cardiac system, the “QRS complex”, the “P wave” and the “T wave.” The P wave is a structure in the ECG signal which corresponds to the depolarization of the atria as the main electrical vector is directed from the SA node to the Atrioventicular Node (AV node). The QRS complex is a structure in the ECG signal that corresponds to the depolarization of the ventricles. Because the ventricles contain more muscle mass than the atria, the QRS complex is larger than the P wave. The T wave is a structure in the ECG signal that corresponds to the “repolarization” or recovery of electronegativity in the ventricles after depolarization.
Heart rate variability (HRV) refers to the beat to beat alteration in heart rate. Heart rate variability can be determined based on electrocardiogram (ECG) signals. The “RR Interval” is the distance between consecutive R peaks in an electrocardiogram signal. The heart rate for a given time period is defined as the reciprocal of an RR interval (in seconds) multiplied by 60. Healthy hearts exhibit a large HRV, whereas an absence of variability or decreased variability is associated with cardiac or systemic dysfunction. The term “cardiac dysfunction”, as used herein, refers to any type of abnormal functioning of the cardiac system including cardiac disease. Several studies have also shown that a reduction in heart rate variability is also predictive of a subject's likelihood of sudden death from cardiac dysfunction.
Another important interval used to diagnose heart health is the QT interval. The QT interval represents the total time needed for the ventricles to depolarize and regain electronegativity. The QT interval varies according to the heart rate and is typically corrected according to the heart rate. If the QT interval is abnormally lengthened or shortened, heart complications, including Torsade de Pointes (TDP) and sudden death can occur. Prolongation of the QT interval can be associated with certain metabolic and disease states, congenital disease states and adverse drug reaction.
Dynamical systems theory is an area of applied mathematics used to describe the behavior of complex dynamical systems, that is, systems whose states evolve with time in a manner which is difficult to predict over the long range. According to dynamical systems theory, systems may be characterized as being deterministic (meaning that their future states are, in theory, fully defined by their initial conditions, with no random elements involved) or non-deterministic (meaning the future states are random or undefined by their initial conditions). A periodic system is a system which deterministically returns to a same state over time. A random system is a system which is non-deterministic. Chaos theory is an area of dynamical systems theory which seeks to describe the behavior of certain dynamical systems that may exhibit dynamics that are highly sensitive to initial conditions. As a result of this sensitivity, the behavior of chaotic systems appears to be random. This behavior happens even though chaotic systems are deterministic, meaning that their future dynamics are difficult to predict even though their future dynamics are fully defined by their initial conditions, with no random elements involved. This behavior is known as deterministic chaos, or simply “chaos”. This chaotic behavior is observed in natural systems, such as weather systems and is hypothesized to be observed in physiological systems including the cardiac system.
It has been proposed that physiological systems act as chaotic systems, even though this hypothesis is contrary to the classical paradigm of homeostasis. In homeostasis, physiological systems self-regulate through adjustments in order to maintain equilibrium and reduce variability. In contrast, this proposed hypothesis conjectures that a healthy physiological system exhibits characteristics of a chaotic system such as sensitivity to slight perturbations. This sensitivity and the associated responsiveness in the physiological system causes the system to produce a large variety of behaviors in the physiological system, such as the high variability/complexity in heart rate observed in subjects without cardiac disease or dysfunction. Conversely, the hypothesis proposes that unhealthy or dysfunctional biological systems are associated with a decreased sensitivity and have less variability in their behavior than the healthy systems. This corresponds to the low heart rate variability observed in subjects with poor heart health.
Early studies conducted by Dr. Chi-Sang Poon (Poon et al. (2001), Poon et al. (1997), Barhona and Poon (1996)) demonstrate that heart rate variability is not caused by random fluctuations but instead complex, deterministic patterns. Accordingly, a number of studies have applied different metrics traditionally used to study chaotic system to electrocardiogram data. Narayan et al. (1998) discovered that the times series RR interval data exhibit unstable periodic orbits (UPOs). A dense set of periodic orbits is indeed a criterion used to assert the deterministic chaotic dynamics of an underlying system. The Lyapunov Exponent is a metric used to characterize how chaotic a dynamic system is. Positive Lyapunov Exponents indicate that a system is chaotic. Unstable periodic orbits are chaotic and therefore are associated with positive Lyapunov Exponents. Similarly, Hashida and Takashi (1984) investigated the nature of the RR intervals during atrial fibrillation and determined that the distribution of the RR interval follows the Erlang distribution.
While these findings strengthen the hypothesis that the correspondence between heart rate variability and heart health is typical of a chaotic system, these studies have failed to provide a deeper understanding of the underlying dynamics of the cardiac system which cause the observed chaotic behavior. Accordingly, these estimates of chaotic behavior alone cannot reliably be used to predict heart health. Therefore, a deeper understanding of the role of chaos in cardiac dynamics is needed in order to develop accurate metrics of heart health and use these metrics in diagnostics.